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Software Engineer – Machine Learning
| Company | Meta |
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| Location | Menlo Park, CA, USA |
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| Salary | $222772 – $240240 |
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| Type | Full-Time |
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| Degrees | Bachelor’s |
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| Experience Level | Mid Level, Senior |
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Requirements
- Bachelor’s degree (or foreign equivalent) in Computer Science, Computer Software, Computer Engineering, Applied Sciences, Mathematics, Physics, or related field and 4 years of experience in the job offered or in a computer-related occupation.
- Experience must include 4 years of experience in the following: Developing and debugging in C/C++ and Java.
- Translating insights into business recommendations.
- Scripting languages such as Perl, Python, PHP, or shell scripts.
- C, C++, C#, or Java.
- Machine learning, recommendation systems, computer vision, natural language processing, data mining, or distributed systems Python, PHP, or Haskell.
- Relational databases and SQL.
- Software development tools: Code editors (VIM or Emacs), and revision control systems (Subversion, GIT, Perforce, or Mercurial).
- Linux, UNIX, or other *nix-like OS as evidenced by file manipulation, advanced commands, and shell scripting.
- Build highly-scalable performant solutions.
- Data processing, programming languages, databases, networking, operating systems, computer graphics, or human-computer interaction.
- Applying algorithms and core computer science concepts to real world systems as evidenced by recognizing and matching patterns from different areas of computer science in production systems.
- Distributed systems.
Responsibilities
- Research, design, develop, and test operating systems-level software, compilers, and network distribution software for massive social data and prediction problems.
- Have industry experience working on a range of classification and optimization problems, e.g. payment fraud, click-through or conversion rate prediction, click-fraud detection, ads/feed/search ranking, text/sentiment classification, collaborative filtering/recommendation, or spam detection.
- Working on problems of moderate scope, develop highly scalable systems, algorithms and tools leveraging deep learning, data regression, and rules based models.
- Suggest, collect, analyze and synthesize requirements and bottleneck in technology, systems, and tools.
- Develop solutions that iterate orders of magnitude with a higher efficiency, efficiently leverage orders of magnitude and more data, and explore state-of-the-art deep learning techniques.
- Receiving general instruction from supervisor, code deliverables in tandem with the engineering team.
- Adapt standard machine learning methods to best exploit modern parallel environments (eg distributed clusters, multicore SMP, and GPU).
Preferred Qualifications
No preferred qualifications provided.
